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By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix(More)
This paper addresses the problem of fault detection filter design for discrete-time Markovian jump singular systems with intermittent measurements. The measurement transmission from the plant to the fault detection filter is assumed to be imperfect and a stochastic variable is utilized to model the phenomenon of data missing. Our attention is focused on the(More)
This paper is concerned with the problem of exponential stability analysis of continuous-time switched delayed neural networks. By using the average dwell time approach together with the piecewise Lyapunov function technique and by combining a novel Lyapunov-Krasovskii functional, which benefits from the delay partitioning method, with the free-weighting(More)
In this paper, the asymptotic stability is investigated for a class of cellular neural networks with interval time-varying delay (that is, 0<h1<dt<h2). By introducing a novel Lyapunov functional with the idea of partitioning the lower bound h1 of the time-varying delay, a new criterion of asymptotic stability is derived in terms of a linear matrix(More)